Wind power generation via ground wind station and topographical feedforward neural network (T-FFNN) model for small-scale applications

Wind power generation via ground wind station and topographical feedforward neural network (T-FFNN) model for small-scale applications

This study presents the potential of harvesting wind energy in Sarawak, Malaysia based on the ground station and prediction models. A topographical feedforward neural network (T-FFNN) is proposed as an alternative to predict the wind speed in the areas where wind speed measurements are not done. The...

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Bibliographic Details
Main Authors: Salisu Muhammad, Lawan, Wan Azlan, Wan Zaina Abidinl, Thelaha, Bin Hj Masri, Chai, Wangyin, Baharun, Azhaili
Format: E-Article
Language:English
Published: Elsevier Ltd 2017
Subjects:
TA Engineering (General). Civil engineering (General)
Online Access:http://ir.unimas.my/id/eprint/14963/1/Wind-power-generation-via-ground-wind-station-and-topographical-feedforward-neural-network-%28T-FFNN%29-model-for-small-scale-applications_2017_Journal-of-Cleaner-Production.html
http://ir.unimas.my/id/eprint/14963/
http://www.sciencedirect.com/science/article/pii/S0959652616320194
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Institution: Universiti Malaysia Sarawak
Language: English

Internet

http://ir.unimas.my/id/eprint/14963/1/Wind-power-generation-via-ground-wind-station-and-topographical-feedforward-neural-network-%28T-FFNN%29-model-for-small-scale-applications_2017_Journal-of-Cleaner-Production.html
http://ir.unimas.my/id/eprint/14963/
http://www.sciencedirect.com/science/article/pii/S0959652616320194

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